System and methods for an intelligent medical practice system employing a learning knowledge base

ABSTRACT

An intelligent care provider medical practice system which learns the care provider&#39;s preferences and historical diagnosis for predicting and treating patients based on provided information. The system makes use of one or more medical knowledge bases and utilizes artificial intelligence and reasoning to learn the provider&#39;s preferences and tendencies. The system also automatically generates the provider&#39;s notes, treatment plans, and other medical practice actions for treating, billing, and processing the patient after an exam or visit.

CROSS REFERENCE TO RELATED APPLICATION

This application is a divisional of U.S. patent application Ser. No.13/205,564 filed on Aug. 8, 2011 which claims the benefit of U.S.Provisional Application No. 61/371,377 filed on Aug. 6, 2010 entitled “ASystem and Methods for an Advanced Electronic Medical Records SystemEmploying a Learning Knowledge Base”, the entirety of which isincorporated herein.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a system and method for an ElectronicMedical Records system which utilizes a learning function associatedwith a care provider's preferences and tendencies in combination with aKnowledge Base.

2. Description of the Related Art

The present invention relates to electronic medical records (“EMRs”)which are computerized medical records created in an organization thatdelivers care such as a doctor's office. The EMRs are typically part ofa health information system that allows for the storage, retrieval, andmanipulation of the medical records. Typically, EMRs are accessible froma workstation or in some cases through a portable or mobile device whichutilizes a standard graphical user interface to retrieve, view, andupdate or edit the medical records.

EMRs are utilized to help manage a heavy patient load but also to assistthe provider to meet the proper standards and documentation required byinsurance companies before payment. The EMR systems typically requirerecordation of pertinent facts and findings of an individual's healthand medical history including exams, tests, and treatments. A typicalpatient encounter includes a history taking segment, a physical exam, ananalysis and diagnosis, a treatment phase, a medical recorddocumentation phase, and a communication phase which might includeinstructions to the patient or a report to a referring doctor.

Currently, some EMR systems are able to monitor events and analyzepatient data to predict, detect, and perhaps prevent adverse events.Such events might include pharmacy orders, lab results, and other datafrom services provided and from the provider's notes. However, most ofthese systems require significant human intervention which hinders theprovider's speed of attending to patients.

Further, current EMR systems fail to provide a health information systemor an electronic medical records system which automates the generationof the provider's note and learns the provider's preferences andtendencies in diagnosis and order selection as well as plans fortreating patients. Current systems require the provider to eitherdictate notes for transcription, write down notes or type them into anEMR system all of which increase the time to process a patient divertingcritical patient to doctor (or care provider) time.

Therefore, what is needed is a system which learns the doctor'spreferences and tendencies in diagnosing and treating patients whileautomating the generation of the provider's notes, work orders, andcommunication.

SUMMARY OF THE INVENTION

This summary is provided to introduce concepts in a simplified form thatare further described in the detailed description of the invention. Thissummary is not intended to identify key or essential inventive conceptsof the claimed subject matter, nor is it intended for determining thescope of the claimed subject matter.

The present invention overcomes the limitations and issues of existingsystems by providing an electronic medical records system which predictsa diagnosis, exam, work up, and treatment plan from a knowledge base,learns and adjusts the predicted diagnosis, exam, work up, and treatmentplan based on the provider's preferences and tendencies andautomatically generates the provider's medical record notes, workorders, communication and other actions for treating the patient.

Based on a patient's responses to a series of questions including, butnot limited to, the body location of the problem, list ofcomplaints/symptoms, nature of the problem, patient demographics, andpractice location, the system automatically determines potentialdiagnoses based on probabilities utilizing a comprehensive, adaptive,and growing Knowledge Base and an artificial intelligence inferenceengine. Upon the provider's selection of a diagnosis, the systemautomatically generates a provider-specific default note, including butnot limited to the chief complaint, history of present illness, pastmedical history, past surgical history, social history, medication,allergies, physical exam, impression, work orders, and plan. Thisdefault note is then updated by the provider as needed to put in anyadditional information that is specific to the current patient is thatbeing treated. The idea of this invention is to keep this update to aminimum by learning the provider preferences and by utilization ofpatient inputted data through the patient portal. Upon finishing thenote, the system automatically processes health insurance formsincluding automatically suggesting appropriate ICD diagnosis codes andCPT codes, automatically generate internal and external work orders,automatically prints out diagnosis specific patient information,generates electronic medicine prescriptions, and automatically sends outthe provider note to referral providers either via email orelectronic/phone FAX.

Utilizing an established Knowledge Base, that is created by a set ofphysician experts, and various methods of weighing and factoring theresponses captured during a patient visit, the EMR and practicemanagement system of the present invention will be able to generate acomprehensive and customizable Provider's Note which can be used bymultiple departments within a medical practice and shared with otherproviders involved with the patient.

The implementation of the system of the present invention will reducethe total amount of time spent in capturing and generating theProvider's Note and will reduce required time to a quick review, update,and approval of the Note by the doctor or care provider. Upon doctorapproval of the Note the system will then initiate the requiredfunctions of the work order generation process. Such functions mightinclude ordering lab tests, generating prescription forms, preparing athome care notes, generating exercise and dietary information, follow onappointments, insurance forms, billing orders, and any other logicalcare or action which might stem from a patient visit. The system maygenerate the forms or generate the data which may be transmitted to aPractice Management System (“PMS”) for use in billing and insuranceprocessing systems.

The system of the present invention will: (1) capture patientinformation in advance of an exam: (2) retrieve Patient Medical Historyand provide it to the provider for review; (3) retrieve and display aset of dynamic questions based on captured patient information; (4)capture patient and provider responses to the pre-defined questions; (5)analyze the responses and data against the system's knowledge base togenerate an anticipated diagnosis based on probability and artificialintelligence reasoning; (6) provide a ranked order of probable diagnosesto the care provider for selection of an actual diagnosis; (7) generatethe draft doctors note based on this diagnosis and on previously learnedprovider-specific preferences; and (8) upon review and acceptance ormodification of the doctor's note generating the required work ordersbased upon the final diagnosis and procedure selections.

The system of the present invention will also learn how each individualprovider performs certain tasks and will adapt to those tasks orprocesses. All parts of the system allow learning of a provider'sspecific preferences. [First, the probability of a diagnosis given acertain set of patient's answers will change depending on the provider'schoice of the actual diagnosis. Second, for each and every diagnosis,the system will learn and adapt to each provider's preference of thephysical exam, works orders, impression, and plan. These learningactivities will take place in the background after every note has beengenerated.

The system will also integrate with known Practice Management Systems(PMS) and/or Billing systems. The system will employ a dynamic KnowledgeBase that can be general or specific such as an Orthopedic KnowledgeBase. The data analysis and algorithms utilized by the system helpdetermine the value or weight of the responses and analyze thoseresponses against the aggregated knowledge base and the provider'slearned tendencies, preferences, and processes. The system will allowthe central gathering of all learned and adapted probabilities acrossall providers in an effort to determine global probabilities and globalpractice patterns.

The present invention provides an intelligent care provider medicalpractice system comprised of one or more servers; one or more clientcomputers in communication with at least one of the servers; one or moredatabases in communication with the servers, and one or more medicalknowledge bases within the databases or on the servers or computers. Thesystem also includes one or more software applications resident on theservers or computers which provide a graphical user interface whichallows entry of patient information and at least one patient healthproblem from the client computer; where the data is received and acomparative analysis of the patient information and patient healthproblem is performed against the medical knowledge base to identify oneor more diagnosis. The system then calculates the probability of each ofthe identified diagnosis based upon a comparative analysis and thenadjusts the calculated probabilities based upon a doctor or careprovider's historical use profile; and then ranks the diagnoses based onthe adjusted calculated probability. The ranked diagnoses can bedisplayed on the servers or computers and the care provider or doctorcan select one of the ranked diagnoses.

The system of the present invention further records the diagnosisselected by the care provider and adjusts or updates the care provider'sdiagnosis profile. Upon diagnosis selection, the system or softwareresiding on one of the servers or computers generates a Doctor's Note orcare provider note. The Doctor's Note, any work orders, and treatmentplans may be based upon the selected diagnosis, the doctor's preferencesor profile, a suggested plan from the knowledge base, the patientinformation, or a combination of such information.

The system may also generate appropriate billing information and CPTcodes based upon the selected diagnosis, work orders, treatment plan,and Doctor's Note.

The present invention further provides a method for predicting adiagnosis based on a care provider profile comprising the steps of:receiving patient information including at least one medical problem;analyzing the patient information and medical problem against a medicalrelated knowledge base or database; identifying one or more diagnosesbased upon the comparison; calculating the probability of each of thediagnoses; adjusting the calculated probabilities based on the careprovider's historical preferences profile; and ranking the diagnosesbased upon the adjusted probabilities.

The system may also display the ranked diagnosis list on a server orcomputer and may receive a selection of one of the diagnoses from thedoctor or care provider. Upon selection of the diagnosis, the systemwill record the selection and update the doctor's profile and historicalpreferences. The system may also generate a care provider note orDoctor's Note, work orders, treatment plans, and billing informationbased upon the selected diagnosis. The Doctor's Note, any work orders,and treatment plans may be based upon the selected diagnosis, thedoctor's preferences or profile, a suggested plan from the knowledgebase, the patient information, or a combination of such information.

The system may also generate appropriate billing information and CPTcodes based upon the selected diagnosis, work orders, treatment plan,and Doctor's Note.

These and other objects, features, and advantages are evident from thevarious aspects of embodiments of the present invention, as described inmore detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing summary, as well as the following detailed description ofthe invention, is better understood when read in conjunction with theappended drawing. For the purpose of illustrating the invention,exemplary constructions of the invention are shown in the drawings.However, the invention is not limited to the specific methods andinstrumentalities disclosed herein.

FIGS. 1-23 illustrate various systems, processes, and interface examplesof the system of the present invention, wherein:

FIG. 1 illustrates a system diagram illustrating one exemplaryembodiment of the present invention;

FIG. 2 illustrates a system diagram illustrating multiple medicalpractice groups interaction with the main computer system of the presentinvention;

FIG. 3 illustrates a process flow of the EMR system during thepre-appointment initial information gathering stage;

FIG. 4 illustrates a process flow for capturing patient data;

FIG. 5 illustrates a process flow of the EMR system during the knowledgebase questions phase;

FIG. 6 illustrates a process flow of the EMR system during the patientexam stage;

FIG. 7 illustrates a process flow of the EMR system during the ProviderNote generation stage;

FIG. 8 is a graphical user interface of a patient Login screen;

FIG. 9 is a graphical user interface of the Patient Information screen;

FIG. 10 is a graphical user interface of the Patient History screen forentry of past medical problems;

FIG. 11 is a graphical user interface of the Patient History screen forentry of family history and social history information;

FIG. 12 is a graphical user interface of the Current Visit screen forentry of the reasons for the visit;

FIG. 13 is a graphical user interface of the Current Visit screen forentry of the primary and other symptoms;

FIG. 14 is a graphical user interface of the Current Visit screenincluding a graphical representation of body for entry of problemlocation;

FIG. 15 is a graphical user interface of the Current Visit screen forentry of previous treatment;

FIG. 16 is a graphical user interface of the Current Visit screen forentry of responses to additional questions and tests;

FIG. 17 is a graphical user interface of the Appointments displayincluding patient information;

FIG. 18 is a graphical user interface of the screen displaying thesuggested diagnosis list stemming from the knowledge base analysis;

FIG. 19 is a graphical user interface of the screen suggesting variouswork orders for a selected diagnosis;

FIG. 20 is a graphical user interface of the display which provides aneditable version of the doctor's note;

FIG. 21 is a graphical user interface of the Work Order display;

FIG. 22 is a graphical user interface of the medical practice routingslip; and

FIG. 23 is a graphical user interface of the Access Modifiers display.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Particular embodiments of the present invention will now be described ingreater detail with reference to the figures.

Referring now to the diagrams, FIGS. 1 and 2 illustrate an exemplaryembodiment of the system of the present invention. As seen in FIG. 1,the system employs one or more servers 20, 22 which are used forproviding the website or interface of the system, applications foranalysis and data retrieval, as well as the structure and access tovarious databases. The system can include a web server 20 for runningvarious software and applications providing the necessary graphical userinterfaces, login, and logic behind interaction with the website anddata. The logic would include any reasoning, algorithms, calculationsand other analysis the system might perform. The web server 20 would beconnected to a database server 22 such as a server running one or moreSQL or MySQL installations. The database server 22 would have access tovarious databases 26, 27, 28, 29. The databases might include one ormore knowledge base databases such as an Orthopedics Knowledge Base 26,a Pediatrics Knowledge Base database 27, or other knowledge base datasets.

The system would also include a registered user database 28 whichestablishes the users, types of users, and access control for each userlevel. The user database 28 could also include the doctor or careprovider profiles and their historical interaction with the system. Thehistorical interaction would include selection of diagnosis andselection of work orders to learn the doctor's preferences andtendencies for factoring during the diagnosis analysis phase and doctornote generation phase. The servers 20, 22 could also be connected to adatabase 29 with specific patient data such as the historical patientdata and patient demographic data. This access might be local or remoteand the system might pull or retrieve patient data as needed and add itto the EMR records within the system for each patient.

The servers 20, 22 are remotely accessed by the doctor or practice'slocal network 50. The local network 50 of the doctor might include alocal server 55, one or more databases 57, and one or more computers,terminals, or access devices 52, 54. The local network 50 provides ameans for the networked doctors, medical assistants, physicianassistants, billing department, and other individuals within thepractice to access local applications on server 55 and local data 57 aswell as the interact with the remote servers 20, 22 of the system of thepresent invention. The computers or terminals 52, 54 may be desktops,laptops, notebooks, tablets, a kiosk, smart phones, or other electronicdevice for accessing the local network 50 in a secure manner.

Alternatively, the practice need not have a local network configurationand could have the computers or terminals 52, 54 remotely access the EMRsystem of the present invention as well as remotely access all patientdata, billing data and other required applications. The system of thepresent invention might also interact with third party servers orapplications 30 and data 35. Such third party systems might includeaccess to an additional knowledge base, billing software, a CPT codedatabase, or access to additional features and functionality. Suchaccess might include API access to translation and transcriptionsoftware, community functionality for interaction with other doctors andproviders, or access to updated processors such as billing companies.Access between remote systems and components is through electroniccommunication such as through the internet 10. Local access betweencomponents can be through a wired or wireless connection such as throughdirect line or a Wi-Fi connection.

As seen in FIG. 2, the system of the present invention is designed towork with more than one medical practice units. The members of a firstmedical practice group would access their local system through one ormore smart devices 252, 254, 256. The smart devices could be a desktop,laptop, tablet, smart phone or other communication device. The deviceswould mostly likely access the medical practice's system through arouter 259. The router 259 could be a wireless router. The router 259 isconnected to at least one computer or server 285 which includes numerousapplications, folders, and files for interacting with the main system220 of the present invention. The computer or server 285 is alsoconnected to a database 257. The members of a second medical practicegroup would access their local system through one or more smart devices262, 264, 266. The smart devices could be a desktop, laptop, tablet,smart phone or other communication device. The devices would mostlylikely access the medical practice's system through a router 269. Therouter 269 could be a wireless router. The router 269 is connected to atleast one server or computer 295 which includes numerous applications,folders, and files for interacting with the system 220. The secondmedical practice computer or server 295 would be connected to one ormore databases 267.

In the preferred embodiment, each medical practice computer or server285, 295 would include a windows service 282, 292 a messaging queuingservice (MSMQ) 281, 291, an incoming XML folder 283, 293 and an outgoingXML folder 284, 294 for interacting with the company level system 220.

The medical practice computers or servers 285, 295 would communicatewith the company level system 220. The company level system 220 could beone or more computers or servers which contain various softwareapplications and files for interaction with the medical practice systemsand for processing and analyzing data to provide updated data andanalysis to medical practices and practitioners.

In a preferred embodiment, the company level system 220 would include awindows service 222, a web service 224, a message queuing (MSMQ) service221, an XML folder for computations and data received 225, and a medicalpractice XML transmitting folder 223. The company level system 220 wouldbe connected to numerous databases 226, 228. The databases 226, 228would include one or more medical knowledgebase data sets such as anOrthopedics knowledge base or a Pediatrics knowledge base. The databases226, 228 might also have registered user data, patient data,practitioner data and other data sets.

In one exemplary embodiment, the company databases 226, 228 would useSQL and the company level server 220 will have two folders to store xmlfiles. A windows service 222 will run on the company server 220 toprovide XML from the XML folder 225 to the web service 224. The XMLfolder 225 for computations stores data received from the practicegroups which is then analyzed and structured for computational analysisin accordance with the present invention.

The Windows service 222 will also create and store XML files in thePractice XML folders 223 to send to the medical practice groups. The XMLfiles to send are based on the new inserted and updated data in thecompany databases 226, 228. The messaging queuing (MSMQ) service 221 isconfigured to send the xml files in the queue. The Web service 224 isresponsible for performing a create, read, update, and delete (CRUD)operation on the company databases 226, 228 based on the xml received bythe windows service 222.

In use, the medical practice user will use an application resident onthe smart device 252, 254, 256 to access the medical practice computersystem 285, 295. In the preferred embodiment, the user belongs to apractice which will be using a common SQL Server database 257, 267installed on practice server 285, 295. The Windows service 282, 292 willrun on the Practice level server 285, 295 for sending the XML files overthe internet 210 to the Web service 222 running on the Company Server220. The Windows service 282, 292 of the practice is also responsible toperform a create, read, update and delete (CRUD) operation at thepractice level after receiving the xml in the Incoming XML folder 283,293.

The Practice level server 285, 295 will also have two folders to storeXML. The Outgoing xml folder 284, 294 will store all xml files whichwill be created by applications after performing the CRUD operation onthe practice database 257, 267. The Incoming xml folder 283, 293 willstore the xml files which the medical practice group receives from thecompany level system 220.

The Practice level server 285, 295 will also have the message queuingservice 281, 291 configured to provide message queuing for sending XMLto the windows service 282, 292. Whenever any operation will beperformed on a database 257, 267 at the practice level an xml file willbe created and transmitted to the company level system 220. Through thenetwork and systems of the present invention, the local medical practicegroups are able to obtain patient information locally, update EMR files,and interact with the knowledge base data and analytic engine residenton the primary servers 220 and databases 226, 228.

Although the system is comprised of various computers, softwareapplications, and databases the interaction for the user is primarilythrough one or more user interfaces used to obtain and analyze datarelated to a patient's current or historical medical condition. Theinterfaces help establish various procedures and processes for gatheringand analyzing the information. As shown in FIGS. 3-7, the variousmethods and procedures of the present invention are depicted. FIG. 3provides a use flow model for receiving patient data prior to anappointment. In step 300 a patient requests an appointment. Theappointment request is handled by an administrator or front desk personof a clinic or care provider practice who enters the appointment intothe Practice Management System (“PMS”) as shown in step 301. Theappointment information is then received and added to the PMS system instep 302. The patient appointment is created and an HL7 update is sentto that patient's Electronic Medical Record (“EMR”) in step 303. Thepatient appointment entry is received and updated in the EMR system instep 304. In step 305 the patient is provided medical historyinformation and related data in the system for review. The patientupdates any information and enters any new or relevant data in step 306.The information may be entered directly into electronic forms such asmight be available at a portal or kiosk. Alternatively, the patientcould provide such information remotely over the internet through acomputer or the patient could provide the information on paper forms foran administrative assistant to enter. In step 307 the updatedinformation is sent to the EMR system and the patient data is updated.The additional patient data is then retrieved and/or received by the PMSsystem in step 308. On the day of the appointment all data is retrieved,combined and provided to the medical practice in step 309. The retrieveddata may include previously entered health information, information fromother care providers, lab results and other relevant information. In anexemplary embodiment the patient is provided a kiosk or a computerterminal at the medical practice facility to provide the information andresponses.

FIG. 4 shows the process and flow behind the capturing of patient data.The patient schedules their appointment in step 400. In step 401 theappointment is entered or scheduled in the PMS. In step 402, thepatient's appointment data and basic demographic information is receivedby the EMR using Health Level Seven (HL7) standards. The system may usea different health level standard other than HL7 and still fall withinthe scope of the present invention. In step 403 the patient's data isstored in the EMR mainframe or database and available for use andretrieval by the system. In step 404, the patient's appointment detailsare accessed when the patient logs in. In step 405 the patient fills outall details about their health problem prior to the doctor'sexamination. Such information could be filled out using a kiosk or smartdevice. The information could also be entered by an assistant frommanual forms or from responses to questions and interaction with theassistant. In step 406, the updated information is stored in the EMR foraccess by the practice.

In step 407, the medical practice members use the EMR information andPMS information to interact with the patient. Based on the patientinteraction, updated visit information is generated and the careproviders in the practice then provide information related to the visit.The visit information is pushed back into the EMR mainframe or databaseand the visit update is entered into the PMS system (step 409) and sentto the EMR system (step 408).

As seen in FIG. 5, the method and process of the present invention makesuse of a knowledge base. The patient in step 500 provides information ontheir health complaint or issue including their chief complaint and thelocation of the complaint or medical problem. The chief complaint isentered and received by the system in step 501 and the location of thecomplaint is entered and received by the system in step 502. The systemmay allow the patient to enter specific text base responses to thelocation of the complaint or allow the patient to circle, or otherwiseselect, an area of complaint on a displayed image. Such a system enablesthe patient or user to select or circle a body part such as a hand,foot, arm, etc. Then the system would be able to zoom in on the imageand allow the user to identify the specific area of complaint such asthe heel of the foot.

Based upon the response to the chief complaint and location of thecomplaint, the system (step 503) determines an appropriate set ofquestions which are presented to the patient in step 504. The responsesare received from the patient in step 505. Based upon the responses tothe questions, the system in step 506 will weigh each response andcompare it to the Knowledge Base (“KB”) using various methodologies andan analytical engine to determine the probability of diagnosis. Thesystem will then list the potential diagnoses in a descending orderbased on probability. The system may use various analytical processesand artificial intelligence methodologies to determine the probabilitysuch as Bayesian reasoning, fuzzy logic, neutral networks, or any otherlogical method for analyzing the patient's data against the KnowledgeBase and accurately predicting probability of a diagnosis.

In step 507, the ordered list of preliminary diagnoses is then madeavailable for display to the medical assistant or for review by thedoctor during the exam. If the doctor agrees with the preliminarydiagnosis the doctor indicates acceptance of the preliminary diagnosisto the system and the KB (step 508) will process and display thecorresponding work up. The KB information is then used to generate thework order for the medical assistant (step 509). In the event the doctordoes not agree with the preliminary diagnosis the information is thenused by the doctor during the patient exam (step 510) and doctor'sentered diagnosis is fed back into the knowledge base for adjusting theprobabilities for that specific doctor. Thereby, the system is able tolearn each doctor's preferences on identifying diagnosis and treatmentplans. Further, through the aggregated information of many doctors thesystem can identify the most common diagnosis and treatment planshistorically, seasonally, and currently and alter the probabilitiesaccordingly.

FIG. 6 details the typical flow during a patient doctor exam. During atypical exam the medical assistant processes the intake information instep 600. The KB uses the provided health and condition information togenerate an Initial Diagnosis and determine other diagnoses based onprobability (Step 601). The diagnoses are provided to the EMR anddisplayed to the medical assistant in step 602 to generate any workupsor tests to be performed. In step 603, the doctor reviews the diagnosislist from the KB along with other data from the EMR. Such data mightinclude the initial diagnosis, patient's complaint, location ofcomplaint, patient's health history and response to questions, labresults and work orders, suggested tests and analysis, treatments andother data and information which might be useful in the treatment of thepatient.

In step 604, the doctor sees and examines the patient. In step 605, thedoctor is prompted by the system to either accept or reject the initialdiagnosis. If the doctor accepts the diagnosis then in step 606 thesystem identifies the doctor's preferred plan or course of action basedon probability and learned preferences of the doctor. In step 607, thedoctors selected course of action is captured and the KB and associatedalgorithms and analytics account for and adjust the doctors preferredselection criteria and modify the probability calculations accordinglyfor future diagnosis and treatment analytics.

In the event the doctor does not accept the initial diagnosis from theKB in step 605 then the change diagnosis command is received by thesystem and the doctor is prompted to change the diagnosis (Step 608).The doctor may enter a diagnosis or search the system and KB for adiagnosis based upon keywords. The new diagnosis is entered and assessedby the KB to identify the best plan (step 606) or treatment. Further,the doctor entered diagnosis is then used by the system to calculate newICD/CPT codes, update all corresponding data, and update the patientrecord in step 609. The updated information is then displayed (Step 610)on the doctor's note. The doctor is then prompted (Step 611) to approvethe doctor's note. In this exemplary embodiment, the doctor interactswith the system and reviews the probable diagnosis and Doctor's Notethrough a wireless personal computer, tablet or mobile terminal whichthe doctor can utilize while assessing the patient.

When the doctor ultimately selects a diagnosis from the KB the diagnosisacceptance is added to that specific doctor's historical treatmentrecord and utilized by the system for future determination of diagnosisrank or order. This learning function allows the system to adjust boththe weighting of responses and the probability calculations fordetermining anticipated diagnoses.

FIG. 7 depicts the process and flow for generation of the doctor's note.In step 700, the doctor is provided and reviews the EMR of the patient.The doctor then examines the patient in step 701. Upon selection of thediagnosis as previously discussed, the system in step 702 will generatea Doctor's Note which includes the chief complaint, history of presentillness, past medical history, past surgical history, social history,review of systems, medication, allergies, impression, and plan. TheDoctor's Note can be utilized for documentation of the patientencounter, treatment, prescriptions, requested tests, required lab work,insurance information and other information and processes which canimprove the administrative flow of paperwork required by a medicaloffice. The Doctor's Note will be viewable on the screen of the smartdevice or computer and might include sections for the Physical Exam 703,History of Present Illness 704, Past Medical History 705, CurrentMedications 706, Lab or Test Results, and other pertinent informationrelated to the visit and the patient.

The system will also allow the doctor to review various informationaspects on the diagnosis from the Knowledge Base. In addition, theDoctor's Note will be prefilled with the patient's relevant demographicinformation, personal data, and medical data captured through the portalor kiosk or entered by a medical assistant. The doctor will be able tonavigate to various sections of the doctor's note to review, edit, orupdate any information.

Once the doctor finalizes and confirms the diagnosis and procedures instep 707, the system will then automatically generate provider-specificappropriate work orders. In step 708 the various work orders are savedin the system and notifications are sent to appropriate individuals inthe medical practice group or external service providers for furtherprocessing if necessary. Upon generation of the work orders they will beavailable within the system for processing by the medical assistant,physician's assistant, the billing department and any technicians or labassistants. In step 709, the final diagnosis and doctor's noteinformation is added to the EMR data for that patient and is updated atboth the local server and remote mainframe. Finally, in step 710, allbilling and insurance orders are sent to the PMS.

By way of example, the work orders might include scheduling additionalappointments or therapy sessions, ordering lab work, generation ofprescription orders for signature, billing instructions upon checkout,and other relevant orders and procedures. The system of the presentinvention may contain pre-defined procedures and orders but can alsoaccept orders and procedures established by the practice or any specificdoctor.

An additional important aspect of the present invention is the doctor'spreferred method of treatment for each diagnosis. The system includes aself learning or artificial intelligence (AI) component that learns thedoctor's diagnosis selection tendencies as well as the doctor'spreferred work orders and procedures for each diagnosis. Therefore, whena doctor makes a diagnosis the system can pre-select the orders itdetermines the doctor is likely to want and prompt the doctor forapproval or inclusion of the appropriate work orders. This AI componentis also complex in its application as it can apply the AI analysis onmany parameters. Such parameters might include gender, age, race,height, weight, overall health, previous complaints, health history,genetic characteristics, known familial health patterns, and otherrelevant differences in patients. Therefore, the system is sophisticatedenough to understand the doctor may have different procedures and ordersfor a similar complaint and diagnosis based on the patient's profile. Anexample of this might be the difference in work orders, procedures, andtreatment of a sprained knee in a young child compared with an adultwith a history of knee ailments.

To understand the analytical processing and reasoning behind thepredictive diagnoses we can discuss an example using Bayesian reasoning.Suppose we have a disease and a set of n relevant symptoms or chiefcomplaints. If we let D be a random variable that is true if the patienthas the disease and is false otherwise. Similarly, s_(i) is a randomvariable that is true if the patient has (or complains of) symptom i andfalse otherwise. The fact that having the disease causes the patient toexperience the symptoms, and that the symptoms are conditionallyindependent given the disease (i.e., p(s₁, s₂, . . . ,s_(n)|D)=Π_(i)p(s_(i)|D)). That is, the joint probability of having thesymptoms given the disease is equal to the product of the probabilitiesof having each symptom individually given the disease. Or, moreintuitively, if it is known that you have the flu then knowing that youhave chills doesn't provide any additional information about whether youhave a fever.

Suppose for the moment that the system knows the values of all symptoms(i.e., whether the patient complains of them or not). To compute theposterior probability of having the disease given this information wecan solve as follows:

$\begin{matrix}{{p\left( {{Ds_{1}},s_{2},\ldots \mspace{14mu},s_{n}} \right)} = \frac{{p(D)}{p\left( {s_{1},s_{2},\ldots \mspace{14mu},{s_{n}D}} \right)}}{p\left( {s_{1},s_{2},\ldots \mspace{14mu},s_{n}} \right)}} \\{= \frac{{p(D)}\pi_{i}{p\left( {s_{i}D} \right)}}{p\left( {s_{1},s_{2},\ldots \mspace{14mu},s_{n}} \right)}} \\{= \frac{{p(D)}\pi_{i}{p\left( {s_{i}D} \right)}}{\Sigma_{d}{p\left( {s_{1},s_{2},\ldots \mspace{14mu},{D = d}} \right)}}} \\{= \frac{{p(D)}\pi_{1}{p\left( {s_{i}D} \right)}}{{\Sigma_{d}\left( {D = d} \right)}{p\left( {s_{1},s_{2},\ldots \mspace{14mu},{{s_{n}D} = d}} \right)}}} \\{= \frac{{p(D)}\pi_{i}{p\left( {s_{i}D} \right)}}{\Sigma_{d}{p\left( {D = d} \right)}\pi_{i}{p\left( {{s_{i}D} = d} \right)}}}\end{matrix}$

First, the portion of the equation written as p(D|s₁, s₂, . . . ,s_(n)), relates to the probability that the disease has some particularvalue about which I care (i.e., true) given that the symptoms havewhatever values were observed or reported. However, the expressionp(s_(i)|D=d), means the probability that a symptom has whatever valuewas reported given that the disease variable has value d, which isimposed regardless of what is true in the world. Second, to computeexact probabilities using a Bayesian Network (“BN”) structure, thesystem only needs to know the prior probability of the disease (p(D)).Thus, the system needs to known the probability that a randomly selectedpatient in the doctor's office has the disease, and the probability ofhaving each symptom given the disease (p(s_(i)|D) for both D=true andD=false).

Turning our attention to those instances when the system does not knowthe values of all of the s_(i) variables. For example, a patientcomplains of pain but does not answer the question provided by thesystem about redness, or the question is not posed for some reason. Itis important to know the difference between not having symptom i, inwhich case s_(i)=false, and not knowing whether the patient has thesymptom, in which case we don't know the value of s_(i).

Assuming the system does not know the value of just one s_(i) and,without loss of generality, that i=1. The system proceeds as follows:

$\begin{matrix}{{p\left( {s_{2},\ldots \mspace{14mu},{s_{n}D}} \right)} = {\sum\limits_{v \in s_{q}}{p\left( {s_{1},s_{2},\ldots \mspace{14mu},{s_{n}D}} \right)}}} \\{= {\sum\limits_{v \in s_{q}}{{p\left( {s_{q\; 1} = {vD}} \right)}\pi_{i > 1}{p\left( {s_{i}D} \right)}}}} \\{= {1 \times \pi_{i > 1}{p\left( {s_{i}D} \right)}}} \\{= {\pi_{i > 1}{p\left( {s_{i}D} \right)}}}\end{matrix}$

If the system doesn't know the value of a symptom's random variable,then we can remove it from the product. This works only in the specialcase of the BN structure above. The system can follow the same line ofreasoning with more complex BN structures, but in the general casedropping terms in the product does not produce correct results.

To make all of this concrete, let's consider a scenario in which thereare only two diagnosis, paronychia (P) and felon (F), and that everyperson who comes to the doctor's office has one or the other, withparonychia occurring 60% of the time. The baseline knowledge baseindicates that 60% of patients have P, and thus 40% do not. Using amedical related knowledge base and several assumptions we can start todetermine probability. The knowledge base also provides that theprobability of having a symptom is low when the disease is not present.For example, the probability of having no pain with Paronychia is only20% or 0.2 when P=no. Accordingly, the probability of having no painwith something other than Paronychia is 80% or 0.8.

Continuing with our example, let's assume a patient comes into theoffice and indicates they have redness, but answer no other questions.What is the probability they have paronychia? Using values from theknowledge base we can apply them to our analysis and have the following:

${p\left( {P{redness}} \right)} = \frac{{p(P)}{p\left( {{redness}P} \right)}}{{{p(P)}{p\left( {{redness}P} \right)}} + {{p\left( \overset{\_}{P} \right)}{p\left( {{redness}\overset{\_}{P}} \right)}}}$${p\left( {P{redness}} \right)} = \frac{0.6*0.8}{{0.6*0.8} + {0.4*0.2}}$p(Predness) = 0.86

Either the patient has paronychia or they don't. The number in thenumerator is computed assuming the patient has paronychia. The numeratoris divided by the sum of two numbers, one computed assuming the patienthas paronychia (the same one in the numerator) and one computed assumingthe patient does not. The probability is therefore, the fraction of thatsum that comes from the case in which we assume the patient has thecondition. So after seeing that the patient has redness, the probabilityof having paronychia has jumped from the prior of 0.6 to the posteriorof 0.86.

What if we also know that the patient has swelling? The knowledge baseindicates that swelling provides a 70% (or 0.7) of paronychia and 30%(or 0.3) that swelling is indicative of another diagnosis. Therefore,the system can determine the probability using two known conditions as:

$\begin{matrix}{{p\left( {{P{redness}},{swelling}} \right)} = \frac{{p(P)}{p\left( {{redness}P} \right)}{p\left( {{swelling}P} \right)}}{\begin{matrix}{{{p(P)}{p\left( {{redness}P} \right)}{p\left( {{swelling}P} \right)}} +} \\{{p\left( \overset{\_}{P} \right)}{p\left( {{redness}\overset{\_}{P}} \right)}{p\left( {{swelling}\overset{\_}{P}} \right)}}\end{matrix}}} \\{= \frac{0.6 \times 0.8 \times 0.7}{{0.6 \times 0.8 \times 0.7} + {0.4 \times 0.2 \times 0.2}}} \\{= 0.95}\end{matrix}$

More evidence leads to more certainty about the diagnosis. However, ifthe patient says the location is somewhere other than the fingertip wesee a significant adjustment. Since the knowledge base indicates thatp(loc=other|P=yes)=0, and since this term will be included in thenumerator of the probability calculation, the probability that thepatient has paronychia drops to zero.

Now consider the scenario where the existence of felon as a diagnosis ispresent. Some of the parameters for the felon condition include painhaving an 80% probability with injury having a 70% probability. Supposethe patient complains of pain and injury. What is the probability offelon?

$\begin{matrix}{{p\left( {{F{pain}},{injury}} \right)} = \frac{{p(F)}{p\left( {{pain}F} \right)}{p\left( {{injury}F} \right)}}{\begin{matrix}{{p(F)}{p\left( {{pain}F} \right)}{p\left( {{injury}F} \right)}{p\left( \overset{\_}{F} \right)}} \\{{p\left( {{pain}\overset{\_}{F}} \right)}{p\left( {{injury}\overset{\_}{F}} \right)}}\end{matrix}}} \\{= \frac{0.4 \times 0.8 \times 0.7}{{0.4 \times 0.8 \times 0.7} + {0.6 \times 0.2 \times 0.2}}} \\{= 0.90}\end{matrix}$

What is the probability of paronychia?

$\begin{matrix}{{p\left( {{P{pain}},{injury}} \right)} = \frac{{p(P)}{p\left( {{pain}P} \right)}{p\left( {{injury}P} \right)}}{{p(P)}{p\left( {{pain}P} \right)}{p\left( {{injury}P} \right)}{p\left( \overset{\_}{P} \right)}{p\left( {{pain}P} \right)}{p\left( {{injury}\overset{\_}{P}} \right)}}} \\{= \frac{0.7 \times 0.6 \times 0.4}{{0.7 \times 0.6 \times 0.4} + {0.4 \times 0.2 \times 0.2}}} \\{= 0.91}\end{matrix}$

The system now indicates a 90% chance of having one thing and a 91%chance of having another. The system then normalizes by the sum of theprobabilities. Thus, the probability of paronychia is0.91/(0.91+0.90)=0.503 and the probability of felon is0.90/(0.91+0.90)=0.497. Given this information there is limitedreasoning to prefer one over the other. Pain and injury are strongerindicators of felon, but felon has a lower prior probability. In thiscase, the doctor or care provider may decide more evidence is needed.

In terms of analysis, the preferred embodiment will use the Bayesianmethod and routine to determine probabilities. Thus, given a disease, D,and a set of symptoms for which we know the patient's state, K={k1, k2,. . . , km} where each k_(i) is a symptom index, the system will need aroutine posterior(D, K) that takes D and K as inputs and outputs theposterior probability of the disease given the symptoms. The system willfind the right set of probability tables from the knowledge base using Das an index, and then compute the posterior probability using theindicated symptoms and their observed values via the standard equation:

${P\left( {D = {{yes}K}} \right)} = \frac{{p\left( {D = {yes}} \right)}\pi_{i}{p\left( {{s_{k_{i}}D} = {yes}} \right)}}{{{p\left( {D = {yes}} \right)}\pi_{i}{p\left( {{s_{k_{i}}D} = {yes}} \right)}} + {{p\left( {D = {no}} \right)}\pi_{i}{p\left( {{s_{k_{i}}D} = {no}} \right)}}}$

The system can then display the most probable diseases, or all of thediseases for which the posterior probability is above some threshold, orjust the single most probable disease.

In terms of external representations, the system is able to specify thenumber of values each variable can take on, its parents, and itsprobability tables. Since the system is configured of one or moresoftware applications resident on one or more computers or servers whichhandle the calculations and analysis the nomenclature, files,procedures, or calls for performing such functions for paronychia mightlook like the following:

-   -   VAR paronychia    -   VAR infection    -   VAR pain    -   VALUES paronychia T F    -   VALUES infection T F    -   VALUES pain T F    -   PARENTS infection paronychia    -   PARENTS pain paronychia    -   PROBABILITY paronychia=T 0.6    -   PROBABILITY paronychia=F 0.4    -   PROBABILITY infection=T paronychia=T 0.9    -   PROBABILITY infection=F paronychia=T 0.1    -   PROBABILITY infection=T paronychia=F 0.2    -   PROBABILITY infection=F paronychia=F 0.8    -   PROBABILITY pain=T paronychia=T 0.7    -   PROBABILITY pain=F paronychia=T 0.3    -   PROBABILITY pain=T paronychia=F 0.2    -   PROBABILITY pain=F paronychia=F 0.8

The VAR statements name variables. The VALUES statements take a variableas the first argument followed by a list of possible values for thenamed variable. The PARENTS statements take a variable as the firstargument followed by one or more variables that are its parents in theBN. The PROBABILITY statements take a variable/value pair as the firstargument, a list of variable/value pairs after that, and a real valuelast. The first variable/value pair is the variable for which theprobability is specified, and the other variable/value pairs specify astate of the parents.

As described above, for symptoms s₁-s_(n) and disease D, the probabilityof the disease given the symptoms can be computed as follows:

${p\left( {{Ds_{1}},s_{2},\ldots \mspace{14mu},s_{n}} \right)} = \frac{{p(D)}\pi_{i}{p\left( {s_{k_{i}}D} \right)}}{{{p(D)}\pi_{i}{p\left( {s_{s_{i}}D} \right)}} + {{p\left( \overset{\_}{D} \right)}\pi_{i}{p\left( {s_{i}\overset{\_}{D}} \right)}}}$

In the above equation, p(D) is the prior probability of having thedisease, p(D) is the prior probability of not having the disease,p(s_(i)|D) is the probability the patient has symptom s_(i) given thatthey have the disease, and p(s_(i)| D) is the probability the patienthas the symptom given that they do not have the disease. The systemobtains p(D), p( DD), and p(s_(i)| D) from patient information, analysisor from the knowledge base. Rather than computing p(D|s₁, s₂, . . . ,s_(n)) and p( D|s₁, s₂, . . . , s_(n)) for each disease D, the systemcan use a random variable, (i.e. V), that takes values from the set ofall diseases. Suppose, for the sake of an example, that this set isparonychia, felon, and carpal tunnel (ct). Then the expressionp(felon|s₁, s₂, . . . , s_(n)) becomes:

${p\left( {{{felon}s_{1}},s_{2},\ldots \mspace{14mu},s_{n}} \right)} = \frac{{p({felon})}\pi_{i}{p\left( {s_{i}{felon}} \right)}}{\begin{matrix}{{{p({felon})}\pi_{i}{p\left( {s_{i}{felon}} \right)}} + {{p({paronychia})}\pi_{i}{p\left( {s_{i}{paronychia}} \right)}} +} \\{p({ct})\pi_{i}{p\left( {s_{i}{ct}} \right)}}\end{matrix}}$

The only change is that the system can decompose the absence of thedisease into the sum of all of the things that it could be other thanfelon and obviates the need to compute probabilities conditioned on Dfor any DεV.

The reasoning can also be applied to learning the diagnosis. Suppose thesystem indicates based on probability calculations that the patient hasfelon but the doctor chooses paronychia. The system is able to move thebaseline/Bayesian network in the direction of choosing paronychia in thefuture by addition or counting. For example, to estimate p(felon) thesystem counts how many patients had felon or felon patients (FP) and howmany total patients (TP) have been seen and computes the ratio:p(felon)=FP/TP. To estimate p(s_(i)|D) you divide the number of peoplewho had the symptom and the disease by the number of people who had thedisease. The system can update these probabilities as new patients areseen.

Suppose FP and TP are as defined above. Each time a patient comes to theoffice for a new problem you increment TP by 1. Each time such a patientis diagnosed with felon you increment FP by 1. Note that the magnitudesof FP and b are important in determining how much such a new patientwill change p(felon). If FP=1 and TP=2 then p(felon)=FP/TP=½=0.5. A newpatient with felon results in p(felon)=FP/TP=⅔=0.67.

However, if FP=100 and TP=200 then p(felon)=0.5 as before, but a newpatient with felon changes this probability by only a small amount:p(felon)=FP/TP=101/201=0.502. When a patient comes in with a set ofsymptoms and is given a diagnosis by the doctor, the system can do thefollowing: (1) increment the total patient count and the count of thenumber of patients given that diagnosis by 1; and (2) for each symptomthe patient reported, increment the count of the number of patients whoboth reported that symptom and received the diagnosis by 1. Byimplementing such rules, the system causes the prior probability of thatdiagnosis to increase and that of the other, competing diagnoses todecrease while also increasing the p(s_(i)|D) for each symptom.

The net effect makes the given diagnosis more likely (by the BayesianNetwork) in similar situations in the future. Note that if the systemincremented the counts by 2 instead of 1, it is as if we had seen 2patients with the same symptoms and diagnosis and p(D|s₁, s₂, . . . ,s_(n)) will be even larger.

The system can increment the counts by any number an administrator wouldlike to use. However, the larger the increment, the greater the impacton the posterior probability of the diagnosis given the disease. Recallthat the impact on the posterior is a function of both the size of theincrement and the current counts. For a fixed increment, the effect islarger for smaller current counts.

Thus, to control the rate at which the system adapts, the system onlyneeds to alter the size of the update performed each time the doctormakes or selects, a diagnosis. For example, the system could have asystem parameter, which takes on values from 1 to 100. Each time apatient is diagnosed, the counts are updated by the system parameter.The larger system parameter the faster the system will adapt to matchwhat the doctor does as opposed to the baseline of the knowledge base.

The system also employs a similar methodology for learning the careprovider profile for treatment plans. As described above, each treatmentplan has an established baseline probabilities resident in the knowledgebase. For example, there might be 4 groups of things that are performedfor patients that have felon, and there is some probability of choosingeach of the four. Within the four groups, there may be probabilities foreach component of a group. The system can update the priors on eachgroup by seeing what the doctor chooses for a patient, and likewise forthe components of each group. Again, a factor can be used to control howquickly the system adapts to response differences between what thesystem (or baseline) provides and what the doctor decides.

An example of the present invention in use will now be described inconjunction with FIGS. 8-23 which depict access to the features andfunctionality through various graphical user interfaces or screens.

FIG. 8 depicts a login screen or graphical user interface 800. Thesystem login may have the same or different login screens for thevarious users. Those users would include patients, medical assistants,doctors, medical practice administrative assistants, billing assistants,lab workers and technicians and any other logical party that would needand is allowed access to the system or information. The system enableseach of the users to have an identified administrative role which grantsor limits access to what information they can see, review, edit, andadd. In our example, a patient will use login screen 800 to login at thekiosk at the doctor's office. The Login interface 800 may includeinformation fields such as patient number, date of birth, date ofappointment, name of practice and other patient related fields.

The patient, after logging into the system, is presented a series ofscreens or graphic user interfaces, as depicted in FIGS. 9-16, forreviewing, adding, or editing various pieces of information. As seen inFIG. 9, user interface 900 provides an interface for the patient toprovide or edit patient information. The patient information wouldinclude standard fields including name, address, phone, email, date ofbirth, social security number, insurance information and other logicalpatient information. The Patient Information display 900 may includename, address, phone, social security number, date of birth, gender,email, and other fields along with tabs for patient history and currentvisit.

As seen in FIGS. 10 and 11, the patient is provided a user interface1000 and 1100 for providing patient historical information. As seen oninterface 1000 the patient may input and review past medical problems.On interface 1100 the patient may provide additional family history andsocial history information. In the preferred embodiment, the patientreviews and enters the information directly. However, an administrativeor medical assistant can enter the information for the patient. Afterthe various patient and history information is reviewed and entered intothe system the patient provides information on the reasons for theircurrent visit.

As seen in FIGS. 12-16, the patient is provided a series of screens oruser interfaces 1200, 1300, 1400, 1500, 1600 for providing informationspecific to the current visit. On interface 1200 the user identifies ifthere is a new problem of injury. In our example, our patient has a newproblem related to a lump or mass with pain near his wrist. On interface1300 the user can identify the primary symptom and other symptoms. Oninterface 1400, the user interacts with a series of interactive imagesas viewed in window 1402. Through the images in the window 1402, theuser clicks on or selects the area related to the problem. As ourexample continues, the patient has identified the area as the front ofthe right wrist, palm, and forearm.

On screen 1500 the patient can provide information on whether certainmedications of treatments have been beneficial in treating the pain. Onscreen 1600 a series of additional questions may be presented to thepatient to help the system and doctor more accurately identify anappropriate diagnosis. The user interface screens depicted in FIGS. 8-16are examples of several interfaces which can be employed but are clearlynot intended to limit the number and types of interfaces which could beemployed by the system to enable the patient or an assistant to enterinformation related to the patient or problem.

Once the information has been entered, the system then uses theinformation and responses to questions and analyzes the data against oneor more knowledgebase sets. Through the analytical analysis the systemgenerates a list of potential diagnoses based on probability. Thecalculated probabilities are then used to rank and list the potentialdiagnosis for use by the medical practice doctors and medicalassistants.

As seen in FIG. 17, when a doctor or medical assistant logs into thesystem they are provided one or more user interfaces 1700 where they canview scheduled appointments, previous appointments or upcomingappointments. By selecting one of the patients or files from screen 1700the doctor is presented with all pertinent information of the patientand can view their historical health information as well as the reasonsfor their current visit. Thus the system enables the doctor to reviewinformation already on the patients EMR as well as the specific reasonsand information on the purpose of the scheduled appointment. Further,the system can display to the doctor a potential diagnosis to list ofdiagnoses.

As seen in FIG. 18, the doctor is presented with a user interface 1800which provides a window 1802 which lists the suggested disease ordiagnosis based upon the knowledgebase. The doctor may select thesuggested diagnosis or may add a suggested disease or diagnosis whichwill then be displayed in the provider suggested disease window 1804.

Once the doctor selects or approves the diagnosis, the system analyzesthe knowledgebase and the doctor's preferences to determine and providethe suggested workup. As seen in FIG. 19, the doctor is provided agraphical user interface screen 1900 which indicates various tests whichthe doctor selects or approves. The activities most likely to beselected such as the various tests, procedures, medications or otherresponse may be activity may be pre-checked and are merely seeking thedoctor's approval or confirmation.

For our example, based on the patients lump/mass and swelling and painin the wrist, the system suggested to the doctor in screen 1800 that thepatient may have “de quervain's tenosynovitis.” On interface 1900 thesystem indicates that a 3 view X-ray of the wrist is suggested as one ofthe workup procedures.

Another important aspect of the present invention is the ability of thesystem to generate the Doctor's Note upon completion of the examinationand planned method of treatment. As seen in FIG. 20, after selection ofthe diagnosis and treatment, the system generates the various workuporders and the doctor's note are displayed on interface 2000. Thedoctor's note includes pre generated letters to referring doctors, notesto lab technicians for tests to be performed, and any other logicalstep. The doctor may edit the note directly prior to finalizing the noteand checking out the patient.

Once the note has been finalized, the system generates all of the ordersand determines all of the appropriate CPT codes and billing information.As seen in FIG. 21 on interface 2100, the system provides a detailedview of all pending work orders. However, the system also tracks workorders in process, those completed and enables users to identify overdueorders. As seen in FIG. 22 on interface 2200, the system furtherprovides an electronic routing slip with appropriate CPT codes which canbe used by the medical practice to route the patient and work orders tothe various departments for proper processing. FIG. 23 provides a screencapture 2300 of the various work order procedures required to beperformed for use in appropriate billing systems. By tracking the CPTcodes throughout the process, the system can automatically generate theinformation needed for submission to insurance and the client forpayment.

As previously discussed, the system can use various analyticalmethodologies to determine or estimate the probability that a diagnosisis true. In the preferred embodiment, the system will use a Bayesianmethodology such that it will use an iterative process in which thecollection of new evidence repeatedly modifies an initial confidence ina determined probability of a diagnosis. The system has the ability toapply this reasoning in various aspects and analysis. The system can usenew data on the diagnosis and treatment plans selected by a specificdoctor to modify and learn that doctor's preferences on both diagnosisand treatment. The system can also collectively use the data of all or asubset of doctors to repeatedly modify the baseline Knowledge Baserecommendations for diagnosis and treatments. Further, the system canuse a subset of data specific to time or location to repeatedly modifythe baseline Knowledge Base recommendations for diagnosis and treatmentsor to modify a doctor's preferences. Such functionality is useful ininstances where diagnosis may change with seasonal differences or wherethe KB identifies an increased diagnosis of a certain illness within acertain location and can increase the probability of such an illness(such as during a flu outbreak).

As the doctor begins using the system the doctor has the option of usingthe baseline of the knowledge base of the system or may choose not tostart with a baseline. If the doctor does not use a baseline then thesystem will not make any diagnosis recommendations until the doctor hasstarted entering diagnosis for patients. The data related to thepatients and the doctors diagnosis and treatments are added to buildthat doctor's specific knowledge base. As the doctor sees more patients,the data and symptoms of new patients are then analyzed against thatspecific doctor's knowledge base and the system determines theprobability of diagnosis and present such to the doctor as describedabove.

In the preferred embodiment, if the doctor uses the system baselineknowledge base, the doctor is presented with list at least the three (3)diagnoses. Each diagnoses is associated with a preset probability valuegiven the patient, provider, and practice factors. An example would be aprobability like 7 out of 10 or (0.7). When the doctor selects adiagnosis, the system then incrementally changes the weights by someamount (i.e., add 0.01) of these factors so to increase the probabilitythis diagnosis is predicted in the future if similar factors areencountered. For all unselected diagnoses, the system can incrementallydecrease the weights by some amount (i.e. subtract 0.01). For example,when the doctor does not select the 1^(st) diagnosis but selects analternative diagnosis (2^(nd) diagnosis), the system will still increasethe weights associated with the selection of the 2^(nd) diagnosis. Thus,the system not only has the option of increasing weights for a selecteddiagnosis but also decreasing weights for the non-selected diagnoses.The doctors also have the ability to control the weighting factor suchthat their decisions can have a much more immediate impact such as byadding or subtracting a higher number (i.e. 0.05) to the probabilityscore. In addition, doctors can establish filters which limit the numberof diagnosis presented to only those above a threshold value. Forexample, the doctor may only want to have diagnosis with a probabilityabove 0.5 or 50% displayed. As more are patients seen and more data andsymptom scenarios are added the system builds an adjusted knowledge baseengine for each doctor factoring in that doctor's preferences andhistorical diagnosis decisions.

The system also learns the doctor's preference on treatments andtreatment plans including tests, medications, therapy, and consults.Again the doctor may choose to use the system's baseline treatment planor build their own baseline by informing the system of preferred orprescribed treatment plans. Again, the system will track anymodifications to the baseline and adjust accordingly or will build abaseline if the practitioner started without use of the system baseline.The doctor may create their own treatment plans which are pre-installedinto the system prior to seeing patients.

Another major benefit of the present invention is the ability for thesystem to adjust the suggested treatment plans based on patient specificfactors. Such factors might include insurance approvals, allergies tospecific medications, medication recalls, and other factors. Since thesystem allows the medical practice to apply rules and logic to thetreatment plans based on many factors the system can recommendalternative treatment plans for patients with the same diagnosis butdifferent insurance or other factors. The system can also narrow ortailor or filter the knowledge base to doctors within a specific region(for example, the state of Maryland) to determine the optimal treatmentplans based on what other doctors in that region are prescribing withpatients with similar insurance and health concerns. The doctors mayalso choose to have some diagnosis use the knowledge base suggestedtreatment plans while other diagnosis use a doctor defined treatmentplan or no pre-set treatment plan.

Another aspect of the present invention is the alert or notificationcomponent. As described herein, based on the diagnosis the doctor'spreferences with regards to treatment plans through the baseline plan,adjusted and learned baseline, pre-defined plan, or no set plan variouswork orders are generated for treatment plans, tests, medications andthe like. The notification component is able to alert and notify thevarious constituents in the patient life cycle of tasks needed to beconducted as well as notification of completion of those tasks. Suchnotifications can be used to automatically schedule appointments such asappointment for an x-ray and follow-up appointments once the x-rays areavailable for doctor review. The alerts and notifications can also beused by the medical practice groups to manage resources.

Another important aspect of the present invention is the ability for thesystem to allow for the creation of text into the Doctor's Note throughthe provider's voice. One way is to allow the recording of theprovider's voice into audio files. These audio files are referenced bypatient, provider, date of service, and location with the Doctor's Note.The audio file that can be transcribed into text by an outside medicaltranscriptionist or outside voice recognition software and beautomatically inserted back into the Doctor's Note. The second way isthrough the use or integration of voice recognition software such asDragon Naturally Speaking Medical. This system enables doctors todictate additions/modifications to a doctor's note and get immediatevoice to text translation directly into the Doctor's Note. Thus, adoctor can quickly input and finalize the note for more efficientpatient processing, and to allow for more time for actual treatment ofpatients.

The system also enables doctors to generate surgical work orders whichcan be set to automatically trigger various rules including schedulingsurgical room time, anesthesiologists and other factors. The system mayalso be integrated with digital prescription systems such that apatient's preferred pharmacy can be notified of a prescription that needto be filled automatically. This can also be useful in tracking currentmedications that a patient may be taking to avoid medicinal conflicts.The system has the ability to interact with the digital prescriptionsstandards as well and integrate all data back to the patients EMR.

The foregoing examples have been provided merely for the purpose ofexplanation and are in no way to be construed as limiting of the presentmethod and product disclosed herein. While the invention has beendescribed with reference to various embodiments, it is understood thatthe words which have been used herein are words of description andillustration, rather than words of limitation. Further, although theinvention has been described herein with reference to particular means,materials, and embodiments, the invention is not intended to be limitedto the particulars disclosed herein; rather, the invention expands toall functionally equivalent structures, methods and uses, such as arewithin the scope of the appended claims. Those skilled in the art,having the benefit of the teachings of this specification, may affectnumerous modifications thereto and changes may be made without departingfrom the scope and spirit of the invention in its aspects.

It will be recognized by those skilled in the art that changes ormodifications may be made to the above described embodiment withoutdeparting from the broad inventive concepts of the invention. It isunderstood therefore that the invention is not limited to the particularembodiment which is described, but is intended to cover allmodifications and changes within the scope and spirit of the invention.

What is claimed is:
 1. An medical practice system comprising: at leastone processor based device; at least one database in communication withthe at least one processor, wherein the at least one database has atleast one medical knowledge base; at least one software applicationresident on the processor based device which functions to allow thesystem to: receive a plurality of patient information and at least onepatient health condition; analyze the plurality of patient informationand at least one patient health condition with the at least one medicalknowledge base to identify one or more diagnosis; calculate theprobability of each of the one or more diagnosis based upon theanalysis; adjusts the calculated probabilities based upon a careprovider profile, wherein the care provider profile is a learneddiagnosis profile based upon at least one previously presented diagnosiswhich was not selected; and ranking the diagnoses based on the adjustedcalculated probability.
 2. The system of claim 1, wherein the learneddiagnosis profile is based upon at least one previously selecteddiagnosis.
 3. The system of claim 2, wherein the system records theselected diagnosis of the care provider and adjusts the care providerdiagnosis profile.
 4. The system of claim 1, wherein the system recordsthe presented diagnosis which was not selected and adjusts the careprovider diagnosis profile.
 5. The system of claim 1, wherein the systemreceives a selection of one of the ranked diagnosis.
 6. The system ofclaim 5, wherein the system generates, based upon the care providerprofile, at least one of: a care provider note, a treatment plan, a workorder, and billing information.
 7. A system for executing a computerprogram to provide a care provider medical diagnosis list, the systemcomprising: at least one memory device storing the computer programthereon and at least one medical information related database, and aprocessor which: receives a request to run the program; receives inputdata containing a plurality of patient information and at least onepatient health condition; executes the program to generate one or morediagnosis based on the received data, wherein each diagnosis has anassociated probability, adjusts the probability of each diagnosis basedon a care provider profile, wherein the care provider profile is basedupon at least one previously presented diagnosis which was not selected;ranking the diagnoses based on the adjusted probability to create a careprovider specific diagnosis list; and transmitting the care providerspecific diagnosis list to a client device.
 8. The system of claim 7,wherein the learned diagnosis profile is based upon at least onepreviously selected diagnosis.
 9. The system of claim 8, wherein thesystem records the selected diagnosis of the care provider and adjuststhe care provider diagnosis profile.
 10. The system of claim 7 whereinthe system records the one or more presented diagnosis which was notselected and adjusts the care provider diagnosis profile.
 11. The systemof claim 7, wherein the system receives a selection of one of the rankeddiagnosis.
 12. The system of claim 7, wherein the system generates atleast one of: a care provider note, a treatment plan, a work order, orbilling information.
 13. A method for predicting a diagnosis based on acare provider profile comprising the steps of: receiving patientinformation by a computer processor running at least one computerprogram, wherein the patient information includes at least one medicalcondition; analyzing, by the program, the patient information against atleast one medical information database in communication with thecomputer processor; identifying, by the program, at least one diagnosiswithin the knowledge database based upon the analysis; obtaining, by theprogram, the probability of each of the at least one diagnosis;adjusting, by the program, the calculated probabilities of eachdiagnosis based on the care provider profile, wherein the care providerprofile is a learned profile based upon at least one previouslypresented diagnosis which was not selected; and ranking, by the program,the diagnosis based upon the adjusted probabilities.
 14. The method ofclaim 13, further comprising the step of transmitting, by the program,the ranked probabilities to a client computer.
 15. The method of claim14, further comprising the step of receiving a selection of one of theranked diagnosis from the client computer.
 16. The method of claim 15,further comprising the step of adjusting, by the program, the careprovider diagnosis profile based upon the selected diagnosis.
 17. Themethod of claim 15, further comprising the step generating, by theprogram, at least one of: a care provider note, a treatment plan, a workorder, or billing information.
 18. A system for executing a computerprogram to provide a care provider medical diagnosis list, the systemcomprising: at least one memory device storing the computer programthereon and at least one medical information related database, and aprocessor which: receives a request to run the program; receives inputdata containing a plurality of patient information and at least onepatient health condition; executes the program to generate one or morediagnosis based on the received data, wherein each diagnosis has anassociated probability, adjusts the probability of each diagnosis basedon a care provider profile, wherein the care provider profile is basedupon at least one previously selected diagnosis and at least onepreviously presented diagnosis which was not selected; ranking thediagnoses based on the adjusted probability to create a care providerspecific diagnosis list; and transmitting the care provider specificdiagnosis list to a processor based client device; and receiving aselection from the client device of one of the diagnosis.
 19. The systemof claim 18, wherein the system records the selected diagnosis of thecare provider and adjusts the care provider diagnosis profile.
 20. Thesystem of claim 18 wherein the system records the one or more presenteddiagnosis which was not selected and adjusts the care provider diagnosisprofile.
 21. The system of claim 18, wherein the system generates atleast one of: a care provider note, a treatment plan, a work order, orbilling information.